Top Machine Learning Jobs in Toronto

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Own the ML Platform product roadmap supporting model training, evaluation, deployment, serving, monitoring, and AI capabilities. Translate the needs of applied scientists and ML engineers into priorities, proposals, and platform capabilities while balancing reliability, cost, scalability, and technical debt. Lead discovery, evaluate build-versus-buy options, coordinate cross-functional dependencies, drive platform adoption, and support migrations from legacy tools. The role requires technical product management experience and practical understanding of production machine learning systems.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Develop and implement safety framework methodologies for autonomous trucking readiness decisions. Build high-signal safety datasets across simulation and closed-course testing, establish feedback loops from road monitoring and safety events, validate safety evaluation pipelines, and create traceable safety artifacts. The role also requires cross-functional collaboration, technical mentorship, data analysis, statistical evaluation, and evidence-based safety case development for autonomous vehicles.

Own the Falcon Writer product roadmap and deliver AI-powered authoring features for regulated life sciences content. Translate customer workflows into product requirements, collaborate with engineering and QA, define success metrics, conduct customer discovery, and ensure quality, compliance, verification, and human review. The role requires hands-on product management, AI feature experience, strong stakeholder communication, and comfort solving ambiguous problems with cross-functional teams.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Leads the design, development, deployment, and commercialization of enterprise AI capabilities for the ARGUS Intelligence Platform. Builds LLM applications, agentic workflows, intelligent data extraction, AI analytics, and domain-specific models. Establishes AI/ML engineering standards, governance, testing, deployment, and reliability practices; evaluates emerging technologies; guides architecture and model strategy; mentors engineers; and collaborates with Product, Engineering, Architecture, Data, and Analytics teams.

Leads pharmacovigilance workflow expertise for Veeva Falcon, advising product and engineering teams on safety case processing, reconciliation, AI calibration, and compliance. Translates PV industry trends into automation strategies, engages early adopter customers, and develops complex scenarios to stress-test platform capabilities. Bridges hands-on drug safety operations with technical product development and executive customer engagement.

Own the roadmap, quality bar, evaluation frameworks, and product outcomes for Caseware’s AI agents, agent builder, eval builder, and agentic memory. Partner with applied scientists, engineers, auditors, and accounting firms to validate outputs, define acceptance criteria, measure product health, and determine what ships. Translate practitioner workflows and failure modes into actionable requirements while monitoring AI advancements and coordinating delivery across technical teams.

Analyze claim reserve adequacy using actuarial methods and advanced machine learning models in Python. Support quarterly reserve committee and year-end valuation reporting, maintain internal and external monitoring reports, and provide financial performance insights from a reserving perspective. Partner with cross-functional teams to assess portfolio impacts and improve processes. The role requires actuarial exam progression, strong analytical and communication skills, and proficiency in Excel and VBA.

As a Product Manager, you'll lead product vision and strategy, utilizing customer insights and technology to create innovative solutions while collaborating with multiple teams.

Build and maintain Safari AI’s cloud infrastructure, including production services, system design, reliability, documentation, and security compliance. The role sets technical direction for the cloud stack, collaborates with engineering and operations teams, improves development processes, and delivers incremental features. Candidates should have 4–6 years of software engineering experience, public cloud expertise, and experience taking complex services from concept through production. Streaming processing and machine learning experience are advantageous.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Paid entry-level opportunities supporting the Toronto Blue Jays across scouting, player development, advance scouting, research and development, baseball systems, data engineering, app development, and performance science. Responsibilities may include organizing baseball data and video, operating technology, preparing reports, conducting analytics and research, developing internal applications and databases, and supporting team logistics. Candidates should demonstrate baseball knowledge, analytical and organizational ability, communication skills, and flexibility to work evenings, weekends, and holidays.

Lead independent validation and ongoing monitoring of complex financial and analytics models, including ALM, loss forecasting, loan transition, corporate planning, and engagement models. Identify weaknesses, communicate findings, guide remediation, and strengthen model risk governance. Partner with Finance, Quantitative Research, Growth Analytics, Internal Audit, Internal Controls, Accounting, and Compliance on validation, audit, regulatory, and examiner matters.

Develops communications strategy and thought leadership for cybersecurity, identity, access management, and AI topics. Responsibilities include media relations, executive communications, contributed content, event and speaking strategies, rapid response, narrative development, journalist engagement, and communications measurement. The role translates complex technical concepts into credible messaging for developer, business, and security audiences while collaborating with marketing, product, engineering, security, legal, sales, executives, and agencies.

Lead positioning, messaging, go-to-market strategy, launches, channel enablement, competitive intelligence, and analyst relations for Sophos’ AI cybersecurity solutions. Translate complex technical capabilities into buyer- and partner-focused narratives, build launch assets and sales tools, and coordinate cross-functional execution across product, sales, marketing, and research teams. The role also develops customer feedback loops, competitive battlecards, and category-defining market perspectives.

Lead product strategy for consumer credit, underwriting, merchant pricing, and loan economics. Define features, specs, and metrics; partner with engineering, analytics, design, commercial, legal, and capital to launch and scale products across channels and geographies.

Design, develop, and evaluate large-scale multimodal foundation models for biological research. Build hybrid LLM and graph neural network architectures, relational foundation models, and distributed multi-GPU training systems. Apply deep learning theory to biological data, transition research prototypes into scalable production systems, publish peer-reviewed research, and mentor junior staff while collaborating across scientific and engineering teams.

Own strategic enterprise customer relationships, driving adoption, retention, measurable value, and expansion. Lead success reviews and executive engagements, translate supply chain challenges into solution strategies, build prototypes in Lyric Studio, train customer teams, coordinate internal stakeholders, and identify growth opportunities. The role requires deep supply chain expertise, technical fluency in modeling, planning, or AI/ML, strong executive presence, and up to 30% travel.

Provide legal support for commercialization and product development of an AI-powered ad tech platform: draft and negotiate commercial agreements, manage IP portfolio and complex IP litigation, advise on AI, data, and privacy matters, counsel product teams on compliance and IP protection, and develop automated legal workflows.